Hi,
I am about to write functions for multivariate kernel densitiy estimation
with mixed categorical and continuous date (accoring to Jeff Racine and Qi
Li), and the leave-one-out window esitmation needs a lot of computation.
I am now optimizing the code performance and therefore fhe following
questions:

As R uses call-by-value for functions, is it computational expensive to pass
large matrices in function arguments?

(i.e. are they really copied and does this need much computing time?) Is it
maybe better to work with locally visible variables and nested functions in
the optimized code?

I have already used Rprof (and I could speed up the code a lot by the
information from Rprof), but it does not tell me about that.

Thank you for your hints!

Axel

________________________________________
Fraunhofer Institut fuer
Arbeitswirtschaft und Organisation (IAO)
Dipl. Inf. Axel Benz
Nobelstr. 12
D-70569 Stuttgart
Germany
Tel. +49(0)7119702289
Fax. +49(0)7119702192
mail: mailto:axel.benz@iao.fhg.de
www: http://www.vis.iao.fhg.de
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